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To continue reading this article, please exit incognito mode or log in. Visitors are allowed 3 free articles per month without a subscription , and private browsing prevents us from counting how many stories you've read. We hope you understand, and consider subscribing for unlimited online access. The ability to predict the stock market is, as any Wall Street quantitative trader or quant will tell you, a license to print money. The system was developed by Robert P.
Schumaker of Iona College in New Rochelle and and Hsinchun Chen of the University of Arizona, and was first described in a paper published early this year. Both researchers continue to experiment with and enhance the system - more on that below. Software that analyzes textual financial information - quarterly reports, press releases, news articles - is nothing new.
Schumaker and Chen get around these issues by first radically shrinking the amount of text their system has to parse by boiling down all the financial articles the system ingests into words falling into specific categories of information.
Interestingly, these techniques and categories derive from classification schemes described at the 7th Message Understanding Conference , held in , which was a Defense Advanced Research Projects Agency project to create new and better ways to extract information and meaning from texts. At the time, they were concentrating on terrorist activities in Latin America, airplane crashes, rocket and missile launches and other things relevant to national security.
Using a machine learning algorithm on historical data, they look for correlations that can be used to predict future stock prices. Further work with the AZFinText system has revealed oddities that may or may not remain relevant as researchers continue to apply it to other bodies of historical stock market and financial news data.
For example, in a paper described on June 6 at the Computational Linguistics in a World of Social Media workshop, Schumaker went fishing for the Verbs most likely to cause a stock to move up or down in the next 20 minutes, and came up with a list of terms that had some power to move stock prices. The five verbs with highest negative impact on stock price are hereto, comparable, charge, summit and green. While this movement may not appear to be much, the continued usage of negative verbs is additive.
The five verbs with the highest positive impact on stock prices are planted, announcing, front, smaller and crude. Research in human-computer interaction is mentioning science fiction more than ever, a group of scientists has found.
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